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Journal of Geographical Systems

, Volume 20, Issue 3, pp 227–252 | Cite as

A place-based model of local activity spaces: individual place exposure and characteristics

  • Kamyar HasanzadehEmail author
  • Tiina Laatikainen
  • Marketta Kyttä
Original Article

Abstract

Researchers for long have hypothesized relationships between mobility, urban context, and health. Despite the ample amount of discussions, the empirical findings corroborating such associations remain to be marginal in the literature. It is growingly believed that the weakness of the observed associations can be largely explained by the common misspecification of the geographical context. Researchers coming from different fields have developed a wide range of methods for estimating the extents of these geographical contexts. In this article, we argue that no single approach yet has sufficiently been capable of capturing the complexity of human mobility patterns. Subsequently, we discuss that reaching a better understanding of individual activity spaces can be possible through a spatially sensitive estimation of place exposure. Following this discussion, we take an integrative person and place-based approach to create an individualized residential exposure model (IREM) to estimate the local activity spaces (LAS) of the individuals. This model is created using data collected through public participation GIS. Following a brief comparison of IREM with other commonly used LAS models, the article continues by presenting an empirical study of aging citizens in Helsinki area to demonstrate the usability of the proposed framework. In this study, we identify the main dimensions of LASs and seek their associations with socio-demographic characteristics of individuals and their location in the region. The promising results from comparisons and the interesting findings from the empirical part suggest both a methodological and conceptual improvement in capturing the complexity of local activity spaces.

Keywords

Activity space Local activity space PPGIS Modeling Neighborhood Mobility pattern 

JEL Classification

C61 C65 R200 R230 Y80 

Notes

Acknowledgement

We would like to thank Finnish ministry of education and culture as the primary source of funding for this research. This research is also partially funded by Finnish academy as part of PLANhealth Project (13297753). Our special thanks goes to Dr. Suzanne Mavoa and Dr. Peta Mitchel, for their valuable comments during this project. We would like to also thank Briam Amaya Perez for helping us with the graphics used in this paper, as well as all members of KLAKSU meetings for providing us with constructive feedback during the project.

References

  1. Adams RJ, Howard N, Tucker G et al (2009) Effects of area deprivation on health risks and outcomes: a multilevel, cross-sectional, Australian population study. Int J Public Health 54:183–192.  https://doi.org/10.1007/s00038-009-7113-x CrossRefGoogle Scholar
  2. Alidoust S, Bosman C, Holden G et al (2017) The spatial dimensions of neighbourhood: how older people define it. J Urban Des 22:547–567.  https://doi.org/10.1080/13574809.2017.1336057
  3. Arcury TA, Gesler WM, Preisser JS et al (2005) The effects of geography and spatial behavior on health care utilization among the residents of a rural region. Health Serv Res 40:135–155.  https://doi.org/10.1111/j.1475-6773.2005.00346.x CrossRefGoogle Scholar
  4. Botte M (2015) The connection of urban form and travel behaviour: a geo-spatial approach to measuring success of transit oriented developments using activity spaces. Sort 50:500Google Scholar
  5. Broberg A, Salminen S, Kyttä M (2013) Physical environmental characteristics promoting independent and active transport to children’s meaningful places. Appl Geogr 38:43–52.  https://doi.org/10.1016/j.apgeog.2012.11.014 CrossRefGoogle Scholar
  6. Brown G (2016) A review of sampling effects and response bias in internet participatory mapping (PPGIS/PGIS/VGI). Trans GIS 21:39–56CrossRefGoogle Scholar
  7. Brown G, Kyttä M (2014) Key issues and research priorities for public participation GIS (PPGIS): a synthesis based on empirical research. Appl Geogr 46:126–136.  https://doi.org/10.1016/j.apgeog.2013.11.004
  8. Buliung RN, Kanaroglou PS (2006) Urban form and household activity-travel behavior. Growth Change 37:172–199.  https://doi.org/10.1111/j.1468-2257.2006.00314.x CrossRefGoogle Scholar
  9. Buliung RN, Roorda MJ, Remmel TK (2008) Exploring spatial variety in patterns of activity-travel behaviour: initial results from the Toronto Travel-Activity Panel Survey (TTAPS). Transportation (Amst) 35:697–722.  https://doi.org/10.1007/s11116-008-9178-4 CrossRefGoogle Scholar
  10. Dalgard OS, Tambs K (1997) Urban environment and mental health. A longitudinal study. Br J Psychiatry 171:530–536.  https://doi.org/10.1192/bjp.171.6.530 CrossRefGoogle Scholar
  11. Diez Roux AV (2001) Investigating neighborhood and area effects on health. Am J Public Health 91:1783–1789.  https://doi.org/10.2105/AJPH.91.11.1783 CrossRefGoogle Scholar
  12. Diez Roux AV (2004) Estimating neighborhood health effects: the challenges of causal inference in a complex world. Soc Sci Med 58:1953–1960CrossRefGoogle Scholar
  13. Dijst M (1999a) Action space as planning concept in spatial planning. Neth J House Built Environ 14:163–182.  https://doi.org/10.1007/BF02496820 CrossRefGoogle Scholar
  14. Dijst M (1999b) Two-earner families and their action spaces: a case study of two Dutch communities. GeoJournal 48:195–206.  https://doi.org/10.1023/A:1007031809319 CrossRefGoogle Scholar
  15. Dye C (2008) Health and urban living. Science 319:766–769.  https://doi.org/10.1126/science.1150198 CrossRefGoogle Scholar
  16. Ewing R, Cervero R (2010) Travel and the built environment. J Am Plan Assoc 76:265–294.  https://doi.org/10.1080/01944361003766766 CrossRefGoogle Scholar
  17. Faist T (2013) The mobility turn: A new paradigm for the social sciences? Ethn Racial Stud 36:1637–1646CrossRefGoogle Scholar
  18. Farber S, Páez A, Morency C (2012) Activity spaces and the measurement of clustering and exposure: a case study of linguistic groups in Montreal. Environ Plan A 44:315–332.  https://doi.org/10.1068/a44203 CrossRefGoogle Scholar
  19. Flamm MF, Kaufmann V (2006) The concept of personal network of usual places as a tool for analysing human activity spaces: a quantitative exploration. Conf Pap STRC 2006.  https://doi.org/10.1108/02580540410567256
  20. Frank LD, Engelke PO (2001) The built environment and human activity patterns: exploring the impacts of urban Form on public health. J Plan Lit 16:202–218.  https://doi.org/10.1177/08854120122093339 CrossRefGoogle Scholar
  21. Golledge RG, Stimson RJ, Robert J (1997) Spatial behavior: a geographic perspective. Guilford Press, New YorkGoogle Scholar
  22. Hamilton-Baillie B (2000) Home zones. Reconciling people, places and transport. Study tour of Denmark, Germany, Holland and Sweden, p 36Google Scholar
  23. Hasanzadeh K, Broberg A, Kyttä M (2017) Where is my neighborhood? A dynamic individual-based definition of home zones. Appl Geogr 84:1–10.  https://doi.org/10.1016/j.apgeog.2017.04.006 CrossRefGoogle Scholar
  24. Holliday KM, Howard AG, Emch M et al (2017) Are buffers around home representative of physical activity spaces among adults? Health Place 45:181–188.  https://doi.org/10.1016/j.healthplace.2017.03.013 CrossRefGoogle Scholar
  25. Jarv O, Muurisepp K, Ahas R et al (2015) Ethnic differences in activity spaces as a characteristic of segregation: a study based on mobile phone usage in Tallinn, Estonia. Urban Stud 52:2680–2698.  https://doi.org/10.1177/0042098014550459 CrossRefGoogle Scholar
  26. Jenks GF (1967) The data model concept in statistical mapping. Int Yearb Cartogr 7:186–190Google Scholar
  27. Karusisi N, Thomas F, Méline J, Chaix B (2013) Spatial accessibility to specific sport facilities and corresponding sport practice: the RECORD Study. Int J Behav Nutr Phys Act 10:48.  https://doi.org/10.1186/1479-5868-10-48 CrossRefGoogle Scholar
  28. Kestens Y, Lebel A, Daniel M et al (2010) Using experienced activity spaces to measure foodscape exposure. Health Place 16:1094–1103.  https://doi.org/10.1016/j.healthplace.2010.06.016 CrossRefGoogle Scholar
  29. Krizek KJ (2003) Residential relocation and changes in URBAN travel: Does neighborhood-scale urban form matter? J Am Plan Assoc 69:265–281.  https://doi.org/10.1080/01944360308978019 CrossRefGoogle Scholar
  30. Kwan M-P (2009) From place-based to people-based exposure measures. Soc Sci Med 69:1311–1313CrossRefGoogle Scholar
  31. Kwan M-P (2012a) Geographies of Health. Ann Assoc Am Geogr 102:891–892.  https://doi.org/10.1080/00045608.2012.687348 CrossRefGoogle Scholar
  32. Kwan MP (2012b) The uncertain geographic context problem. Ann Assoc Am Geogr 102:958–968.  https://doi.org/10.1080/00045608.2012.687349 CrossRefGoogle Scholar
  33. Kwan M-P, Chai Y et al (2016) Urban form, car ownership and activity space in inner suburbs: a comparison between Beijing (China) and Chicago (United States). Urban Stud 53:1784–1802CrossRefGoogle Scholar
  34. Kyttä M, Broberg A (2014) The multiple pathways between environment and health. Wellbeing. Wiley, Chichester, pp 1–54Google Scholar
  35. Kyttä AM, Broberg AK, Kahila MH (2012) Urban environment and children’s active lifestyle: softGIS revealing children’s behavioral patterns and meaningful places. Am J Health Promot 26:e137–e148.  https://doi.org/10.4278/ajhp.100914-QUAN-310 CrossRefGoogle Scholar
  36. Kyttä M, Broberg A, Haybatollahi M, Schmidt-Thome K (2015) Urban happiness: context-sensitive study of the social sustainability of urban settings. Environ Plan B Plan Des 43:34–57.  https://doi.org/10.1177/0265813515600121 CrossRefGoogle Scholar
  37. Kyttä M, Broberg A, Haybatollahi M, Schmidt-Thomé K (2016) Urban happiness: context-sensitive study of the social sustainability of urban settings. Environ Plan B Plan Des 43:34–57.  https://doi.org/10.1177/0265813515600121 CrossRefGoogle Scholar
  38. Laatikainen T, Tenkanen H, Kyttä M, Toivonen T (2015) Comparing conventional and PPGIS approaches in measuring equality of access to urban aquatic environments. Landsc Urban Plan 144:22–33CrossRefGoogle Scholar
  39. Lee BA, Reardon SF, Firebaugh G et al (2008) Beyond the census tract: patterns and determinants of racial segregation at multiple geographic scales. Am Soc Rev 73:766–791.  https://doi.org/10.1177/000312240807300504 CrossRefGoogle Scholar
  40. Lee NC, Voss C, Fraser AD et al (2015) Does activity space size influence physical activity levels of adolescents? A GPS study of an urban environment. Prev Med Rep 3:75–78.  https://doi.org/10.1016/j.pmedr.2015.12.002 CrossRefGoogle Scholar
  41. Lord S, Joerin F, Thériault M (2009) La mobilité quotidienne de banlieusards vieillissants et âgés: déplacements, aspirations et significations de la mobilité. Can Geogr/Le Géographe Can 53:357–375.  https://doi.org/10.1111/j.1541-0064.2009.00269.x CrossRefGoogle Scholar
  42. Matthews SA (2011) Spatial polygamy and the heterogeneity of place: studying people and place via egocentric methods. Commun Neighb Heal Expand Bound Place.  https://doi.org/10.1007/978-1-4419-7482-2 Google Scholar
  43. Mitchell R, Popham F (2008) Effect of exposure to natural environment on health inequalities: an observational population study. Lancet 372:1655–1660.  https://doi.org/10.1016/S0140-6736(08)61689-X CrossRefGoogle Scholar
  44. Naess P (2012) Urban form and travel behavior: experience from a Nordic context. J Transp Land Use 5:21–45.  https://doi.org/10.5198/jtlu.v5i2.314 CrossRefGoogle Scholar
  45. Newsome TH, Walcott WA, Smith PD (1998) Urban activity spaces: illustrations and application of a conceptual model for integrating the time and space dimensions. Transportation (Amst) 25:357–377.  https://doi.org/10.1023/A:1005082827030 CrossRefGoogle Scholar
  46. Patterson Z, Farber S (2015) Potential path areas and activity spaces in application: a review. Transp Rev 35:679–700.  https://doi.org/10.1080/01441647.2015.1042944 CrossRefGoogle Scholar
  47. Perchoux C, Chaix B, Cummins S, Kestens Y (2013) Conceptualization and measurement of environmental exposure in epidemiology: accounting for activity space related to daily mobility. Health Place 21:86–93.  https://doi.org/10.1016/j.healthplace.2013.01.005 CrossRefGoogle Scholar
  48. Perchoux C, Kestens Y, Thomas F et al (2014) Assessing patterns of spatial behavior in health studies: their socio-demographic determinants and associations with transportation modes (the RECORD Cohort Study). Soc Sci Med 119:64–73.  https://doi.org/10.1016/j.socscimed.2014.07.026 CrossRefGoogle Scholar
  49. Perchoux C, Chaix B, Brondeel R, Kestens Y (2016) Residential buffer, perceived neighborhood, and individual activity space: new refinements in the definition of exposure areas—The RECORD Cohort Study. Health Place 40:116–122.  https://doi.org/10.1016/j.healthplace.2016.05.004 CrossRefGoogle Scholar
  50. Pickett KE, Pearl M (2001) Multilevel analyses of neighbourhood socioeconomic context and health outcomes: a critical review. J Epidemiol Commun Health 55:111–122.  https://doi.org/10.1136/jech.55.2.111 CrossRefGoogle Scholar
  51. Rai R, Balmer M, Rieser M et al (2007) Capturing human activity spaces: new geometries. Transp Res Rec J Transp Res Board 2021:70–80.  https://doi.org/10.3141/2021-09 CrossRefGoogle Scholar
  52. Schipperijn J, Stigsdotter UK, Randrup TB, Troelsen J (2010) Influences on the use of urban green space—a case study in Odense,Denmark. Urban For Urban Green.  https://doi.org/10.1016/j.ufug.2009.09.002 Google Scholar
  53. Schönfelder S, Axhausen KW (2002) Measuring the size and structure of human activity spaces. Transp Res.  https://doi.org/10.3929/ethz-a-004444846 Google Scholar
  54. Schönfelder S, Axhausen KW (2003) Activity spaces: Measures of social exclusion? Transp Policy 10:273–286.  https://doi.org/10.1016/j.tranpol.2003.07.002 CrossRefGoogle Scholar
  55. Schönfelder S, Axhausen K (2004a) Structure and innovation of human activity spaces. Arbeitsberichte Verkehrs-und Raumplan 258:1–40Google Scholar
  56. Schönfelder S, Axhausen KW (2004b) On the variability of human activity spaces. In: Koll-Schretzenmayr M, Keiner M, Nussbaumer G (eds) The real and virtual worlds of spatial planning. Springer, Berlin, pp 237–262Google Scholar
  57. Seliske LM, Pickett W, Boyce WF, Janssen I (2009) Association between the food retail environment surrounding schools and overweight in Canadian youth. Public Health Nutr 12:1384.  https://doi.org/10.1017/S1368980008004084 CrossRefGoogle Scholar
  58. Sheller M, Urry J (2006) The new mobilities paradigm. Environ Plan A 38:207–226.  https://doi.org/10.1068/a37268 CrossRefGoogle Scholar
  59. Sherman JE, Spencer J, Preisser JS et al (2005) A suite of methods for representing activity space in a healthcare accessibility study. Int J Health Geogr 4:24.  https://doi.org/10.1186/1476-072X-4-24 CrossRefGoogle Scholar
  60. Söderström P, Schulman H, Ristimäki M (2015) Urban form in the Helsinki and Stockholm city regions – Development of pedestrian, public transport and car zones (Reports of the Finnish Environment Institute No. 16/2015). https://helda.helsinki.fi/handle/10138/155224
  61. Spielman S, Yoo E (2009) The spatial dimensions of neighborhood effects. Soc Sci Med 68:1098–1105.  https://doi.org/10.1016/j.socscimed.2008.12.048 CrossRefGoogle Scholar
  62. Subramanian S (2004) The relevance of multilevel statistical methods for identifying causal neighborhood effects. Soc Sci Med 58:1961–1967CrossRefGoogle Scholar
  63. Thompson CW, Roe J, Aspinall P et al (2012) More green space is linked to less stress in deprived communities: evidence from salivary cortisol patterns. Landsc Urban Plan 105:221–229.  https://doi.org/10.1016/j.landurbplan.2011.12.015
  64. Vallée J, Chauvin P (2012) Investigating the effects of medical density on health-seeking behaviours using a multiscale approach to residential and activity spaces: results from a prospective cohort study in the Paris metropolitan area. Int J Health Geogr, France.  https://doi.org/10.1186/1476-072X-11-54 Google Scholar
  65. Vallée J, Cadot E, Roustit C et al (2011) The role of daily mobility in mental health inequalities: the interactive influence of activity space and neighbourhood of residence on depression. Soc Sci Med 73:1133–1144.  https://doi.org/10.1016/j.socscimed.2011.08.009 CrossRefGoogle Scholar
  66. Vallée J, Le Roux G, Chaix B et al (2014) The “constant size neighbourhood trap” in accessibility and health studies. Urban Stud.  https://doi.org/10.1177/0042098014528393 Google Scholar
  67. van Kamp I, van Loon J, Droomers M, de Hollander A (2004) Residential environment and health: a review of methodological and conceptual issues. Rev Environ Health 19:381–401Google Scholar
  68. Van Vliet W (1983) Exploring the fourth environment an examination of the home range of city and suburban teenagers. Environ Behav 15:567–588CrossRefGoogle Scholar
  69. Weiss L, Ompad D, Galea S, Vlahov D (2007) Defining neighborhood boundaries for urban health research. Am J Prev Med 32:154–159.  https://doi.org/10.1016/j.amepre.2007.02.034 CrossRefGoogle Scholar
  70. Wong DWS, Shaw S-L (2011) Measuring segregation: an activity space approach. J Geogr Syst 13:127–145.  https://doi.org/10.1007/s10109-010-0112-x CrossRefGoogle Scholar
  71. Zenk SN, Schulz AJ, Matthews SA et al (2011) Activity space environment and dietary and physical activity behaviors: a pilot study. Health Place 17:1150–1161.  https://doi.org/10.1016/j.healthplace.2011.05.001 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Built EnvironmentAalto UniversityAaltoFinland

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